Overview

Demonstrate your machine-learning-jedi-knight skills in this model competition. Predict gender from a tweeter’s meta information and win lots of 🍫🍫🍫.

The competition will end in…




Mission statement

After his overpaid machine learning task force maneuvered itself into obsolescence having developed the perfect algorithm to predict the locations of future crime, Baschi Dürr, head of justice and homeland security department of Basel-Stadt, needed a new project for his machine learning task before Elisabeth Ackerman, president of the governing council, might withdraw his newly-earned funding. And he knew he had to act quickly. To finance a new set of Wegner Swivel Chairs (what else!) for city hall’s conference rooms, Elisabeth Ackerman and Dr. Eva Herzog, head of the city’s financial department, had just announced that any expendable resources were to be invested in cryptocurrencies. Out of a lack of ideas, Baschi Dürr called up his American friends, who had previously given him such a great price on the American crime data set. He learned that they had recently begun to use machine learning for political education with impressive results (truly outstanding!). They explained that, using machine learning, one could uncover a person’s gender or personality to then send that person tailored political information. All too aware of the Swiss democracy’s need for information, Baschi Dürr ordered his machine learning task force to congregate at once not to waste any time: Switzerland was not going to miss out on this amazing opportunity. America first, Switzerland second.

(Names, characters, businesses, places, events, locales, and incidents are either the products of the author’s imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.)

A - Preliminaries

  1. Open your BaselRBootcamp R project. It should already have the folders 1_Data and 2_Code.

  2. Open a new R script. At the top of the script, using comments, write your name and the date. Save it as a new file called Models_competition.R in the 2_Code folder.

  3. Using library() load the set of packages for this practical listed in the packages section above.

## NAME
## DATE
## Modeling competition

library(tidyverse)
library(caret)
  1. With the code below, load the tweets data set and change any character variables to factors.
# Load tweet data
tweets <- read_csv(file = "1_Data/tweets_train.csv")

# change character to factor
tweets <- tweets %>% mutate_if(is.character, as.factor)

B Competition rules

  1. The goal of the competition is to predict with maximal Accuracy whether a twitter user is 'female' or 'male'.

  2. Entering the competition grants you a chance to win lots of 🍫🍫🍫.

  3. To enter the competition, you can submit up to three caret train-object (result of the train() function) containing your candidate model.

  4. To submit the model, first save your model as an .RDS-file named pseudonym_train.RDS, with pseudonym replaced by a pseudonym of your choice. See the code below.

# save train obect as .RDS
saveRDS(my_train,'1_Data/mypseudonym_train.RDS')
  1. Submit your .RDS file (containing your training object) via the following link:
Task type Criterion Performance measure Submission link
Classification tweets (gender) Accuracy Submit candidate

  1. Use any weapon in your arsenal (or caret’s arsenal). Feel free to try different models, use different tuning parameter settings or preprocessing methods, make use of all or some variables. Whatever may lead to the highest prediction Accuracy. Consult the course materials for help.

  2. In order for us to to be able to evaluate and compare the models, you must refrain from any manipulation (or engineering) of features other than those accessible via the preProcess argument in the train() function.

Datasets

File Rows Columns
tweets 2500 23

Note: The tweets data are a (heavily) pre-processed subsets of this original data set from Kaggle.

Variable descriptions
Name Meaning
gender The criterion. Whether the person tweeting was "male" or "female".
year_created The year the person’s twitter account was created.
hour_created The hour of day (1:24h) the person’s twitter account was created.
tweet_count The number of tweets that the person has posted.
retweet_count The number of retweets that the person has posted.
user_timezone The person’s time zone relative to GMT.
name_nchar The number of characters in the person’s twitter name.
name_male 1 if the person’s twitter name contains one of the 1’000 most frequent male baby names in America, 0 if not.
name_female 1 if the person’s twitter name contains one of the 1’000 most frequent female baby names in America, 0 if not.
descr_nchar The number of characters in the person’s twitter account description.
descr_male 1 if the person’s twitter account description contains one of the 1’000 most frequent male baby names in America, 0 if not.
descr_female 1 if the person’s twitter account description contains one of the 1’000 most frequent female baby names in America, 0 if not.
descr_sent Average sentiment score (>0 = positive sentiment) of the person’s twitter account description.
tweet_nchar The number of characters in one randomly chosen tweet by the person.
tweet_male 1 if the randomly chosen tweet contains one of the 1’000 most frequent male baby names in America, 0 if not.
tweet_female 1 if the randomly chosen tweet contains one of the 1’000 most frequent female baby names in America, 0 if not.
tweet_sent Average sentiment score (>0 = positive sentiment) of the randomly chosen tweet.
linkcol_red Red value (1:255) in the link color according to the person’s twitter scheme.
linkcol_green green value (1:255) in the link color according to the person’s twitter scheme.
linkcol_blue blue value (1:255) in the link color according to the person’s twitter scheme.
sidecol_red Red value (1:255) in the side bar color according to the person’s twitter scheme.
sidecol_green Green value (1:255) in the side bar color according to the person’s twitter scheme.
sidecol_blue Blue value (1:255) in the side bar color according to the person’s twitter scheme.

Cheatsheet

Trulli
from github.com/rstudio